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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic.

Ankit Manderna1, Sushil Kumar1, Upasana Dohare2

  • 1School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered Network Intrusion Detection System (NIDS) for securing vehicular ad hoc networks (VANETs). The novel approach achieves 99% accuracy in detecting threats, enhancing road safety.

Keywords:
VANETconvolution neural networkdeep learningintrusion detectionlong short-term memory

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Intelligent Transport Systems

Background:

  • Vehicular ad hoc networks (VANETs) are crucial for intelligent transport systems but face significant security challenges from attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS).
  • Effective Network Intrusion Detection Systems (NIDS) are essential to mitigate these threats and ensure road safety.

Purpose of the Study:

  • To develop an innovative Artificial Intelligence (AI)-based Network Intrusion Detection System (NIDS) specifically designed for the unique security demands of VANETs.
  • To enhance the performance and accuracy of intrusion detection in VANETs by leveraging advanced Deep Learning techniques.

Main Methods:

  • The proposed NIDS utilizes a combination of Deep Learning models: Cascaded Convolution Neural Network (CCNN) for high-level feature extraction and Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification.
  • The Multi-variant Gradient-Based Optimization (MV-GBO) algorithm is employed to optimize both CCNN and SA-BiLSTM models and for feature extraction, further improving detection capabilities.
  • Model performance was rigorously evaluated on established datasets including KDD-CUP99, ToN-IoT, and VeReMi using the MATLAB platform.

Main Results:

  • The AI-based NIDS demonstrated exceptional performance, achieving an accuracy rate of 99% across all evaluated datasets (KDD-CUP99, ToN-IoT, VeReMi).
  • The integration of SA-BiLSTM and CCNN, optimized by MV-GBO, proved highly effective in identifying complex network intrusions within VANET environments.
  • MV-GBO-based feature extraction significantly contributed to the enhanced learning and detection accuracy of the proposed model.

Conclusions:

  • The developed AI-based NIDS, integrating CCNN, SA-BiLSTM, and MV-GBO, offers a robust and highly accurate solution for securing VANETs against sophisticated cyber threats.
  • This research highlights the potential of advanced Deep Learning techniques in addressing critical security challenges in intelligent transportation systems, thereby improving overall road safety.
  • The proposed model's 99% accuracy signifies a substantial advancement in intrusion detection capabilities for safety-critical vehicular networks.